Off Campus Drives

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I’ve been observing something interesting on LinkedIn feeds lately. Posts that gain comments early tend to: – travel further – stay visible longer – attract more meaningful replies It’s rarely random. Early engagement creates momentum. That insight led me to explore platforms like Engazzy, which focus specifically on that first interaction window. Once you notice this pattern, you can’t unsee it. Upvote on Product Hunt + get a free trial 👇 https://www.producthunt.com/products/engazzy engazzy.com

From 0 callbacks to 5 job offers — in 30 days. A few months ago, I was stuck. Dozens of applications. No interviews. Just silence. Then a hiring manager told me: 🕒 “We spend 7 seconds on a resume.” So I made 3 changes: 1️⃣ Told a story, not a list I rewrote my summary to sound human, not robotic. 2️⃣ Added numbers Every bullet point had real impact: ✔ “Increased engagement by 45%” ✔ “Saved 50+ hours/month” 3️⃣ Made it ATS-friendly Clean layout. Simple fonts. Right keywords. Result? 📈 5 job offers in 30 days. Want the resume template I used? Click here to make resume ( Get 30 Days of FREE Premium using this link) : https://bit.ly/3IxOIlp [ Free Job Search Bundle ] *the link will be valid until Aug 31st 2025. Chào

SQL is the most important tool in any Data Analysis or Data Science project! 🚀 But for most professionals some aspects of SQL are hard to understand. 𝗦𝗼, 𝗜 𝗮𝗺 𝗽𝗿𝗼𝘃𝗶𝗱𝗶𝗻𝗴 𝗮 𝟭𝟬 𝗱𝗮𝘆 𝗽𝗹𝗮𝗻 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝘆𝗼𝘂𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝘄𝗶𝘁𝗵 𝗦𝗤𝗟. 𝗗𝗮𝘆 𝟭 - SQL Basics 𝗗𝗮𝘆 𝟮 - Filtering, Sorting and Operators 𝗗𝗮𝘆 𝟯 - Functions and Aggregations 𝗗𝗮𝘆 𝟰 - Joining tables 𝗗𝗮𝘆 𝟱 - Subqueries and Nested Subqueries 𝗗𝗮𝘆 𝟲 - Set Operations 𝗗𝗮𝘆 𝟳 - Working with Dates and Times 𝗗𝗮𝘆 𝟴 - Advanced SQL Features 𝗗𝗮𝘆 𝟵 - Indexes and Performance Tuning 𝗗𝗮𝘆 𝟭𝟬 - Real World SQL Project So, to help you understand each concept easily here's a resource compiling all of them. 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲𝗺 𝗵𝗲𝗿𝗲: https://bit.ly/4jYYaM0

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Excel vs SQL vs Python Which One Should You Master for Data Science? Still relying only on Excel for your data work? While Excel is a great tool for quick analysis, scaling your career in data science requires you to move beyond rows and columns and master tools like SQL and Python. This infographic breaks down common data tasks—from filtering rows to joining tables—and shows how Excel, SQL, and Python (Pandas) handle them. Want to switch from Excel to Data Science? Here’s your roadmap: 1/ Start with SQL – Learn how to query databases, use joins, and aggregate data. 2/ Pick up Python – Especially the pandas library for data wrangling. 3/ Practice Projects – Analyze real-world datasets and visualize insights. 4/ Build Intuition – Understand not just the "how" but the "why" behind data manipulation steps. If you’re serious about a high-paying Data Science role, don’t leave your prep to chance. Remember, you need structured learning with the right mentorship to crack such roles. So, I highly recommend checking out this. Check them here: https://bit.ly/43pl7Sa

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Master Docker for Data Science – From Zero to Production Tired of hearing “It worked on my machine? This guide takes you from setup to advanced deployment using real-world projects. Learn to: 🔸Build reproducible, scalable ML environments 🔸Containerize Python scripts, ML models (TensorFlow & PyTorch) 🔸Optimize Docker images using Alpine & multi-stage builds 🔸Deploy data pipelines like a prO Perfect for Data Scientists & ML Engineers aiming for production-ready workflows. Register here: https://bit.ly/44qTbze

Complete step-by-step syllabus of #Excel for Data Analytics Introduction to Excel for Data Analytics: Overview of Excel's capabilities for data analysis Introduction to Excel's interface: ribbons, worksheets, cells, etc. Differences between Excel desktop version and Excel Online (web version) Data Import and Preparation: Importing data from various sources: CSV, text files, databases, web queries, etc. Data cleaning and manipulation techniques: sorting, filtering, removing duplicates, etc. Data types and formatting in Excel Data validation and error handling Data Analysis Techniques in Excel: Basic formulas and functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, etc. Advanced functions for data analysis: INDEX-MATCH, SUMIFS, COUNTIFS, etc. PivotTables and PivotCharts for summarizing and analyzing data Advanced data analysis tools: Goal Seek, Solver, What-If Analysis, etc. Data Visualization in Excel: Creating basic charts: column, bar, line, pie, scatter, etc. Formatting and customizing charts for better visualization Using sparklines for visualizing trends in data Creating interactive dashboards with slicers and timelines Advanced Data Analysis Features: Data modeling with Excel Tables and Relationships Using Power Query for data transformation and cleaning Introduction to Power Pivot for data modeling and DAX calculations Advanced charting techniques: combination charts, waterfall charts, etc. Statistical Analysis in Excel: Descriptive statistics: mean, median, mode, standard deviation, etc. Hypothesis testing: t-tests, chi-square tests, ANOVA, etc. Regression analysis and correlation Forecasting techniques: moving averages, exponential smoothing, etc. Data Visualization Tools in Excel: Introduction to Excel add-ins for enhanced visualization (e.g., Power Map, Power View) Creating interactive reports with Excel add-ins Introduction to Excel Data Model for handling large datasets Real-world Projects and Case Studies: Analyzing real-world datasets Solving business problems with Excel Portfolio development showcasing Excel skills Best Resources : https://bit.ly/3E5DVgC Hope this helps you 😊

Complete Roadmap to learn Power BI 👇👇 Step 1: Power BI Basics 1. Understand the Power BI ecosystem, including Power BI Desktop and Power BI Service. 2. Learn to import data from different sources (Excel, SQL Server, CSV, etc.). 3. Explore Power Query Editor for data transformation and cleaning. 4. Practice creating basic visualizations like bar charts, line charts, and pie charts. Step 2: Intermediate Power BI 1. Dive deeper into data modeling concepts such as relationships, calculated columns, and measures. 2. Learn about DAX (Data Analysis Expressions) for advanced calculations and aggregations. 3. Explore Power BI visuals and custom visuals for enhanced data visualization. 4. Practice creating interactive dashboards and reports with slicers, filters, and drill-down features. Step 3: Advanced Power BI 1. Master advanced data modeling techniques like role-playing dimensions and many-to-many relationships. 2. Learn about Power BI Service features such as workspaces, datasets, and dataflows. 3. Explore Power BI administration and security settings. 4. Practice optimizing performance and refreshing data in Power BI reports. I have curated the best interview resources to crack Power BI Interviews 👇👇 https://bit.ly/3E5DVgC Hope you'll like it Like this post if you need more resources like this 👍❤️

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*Coding is easy to start, but staying consistent? That’s the real challenge.* We all begin with motivation, but after a few days, distractions take over. What really helps is practicing with others, in a structured way. That’s why I found Code with Vivek interesting. It’s not another course—it’s a free live coding session every night at 9 PM, where you actually code with guidance. No overcomplicated lectures, just real practice. And the best part? It’s led by Vivekanand Vivek, who: ✅ Worked at Amazon as a software engineer ✅ Is an IIT graduate with 8+ years of experience ✅ Has taught 20,000+ students If you’ve been struggling to stay on track, this might be what you need. Join here: https://zoom.us/meeting/register/KfsHTGJYQFe1Dvytv4RqGg. Join the WhatsApp community now and learn from Vivek Sir : https://chat.whatsapp.com/CEuTwRWceCtE7DxXCQ0OJ3 Try it out tonight, and bring a friend. it’s always more fun to learn together!

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This Leet Code SQL 50 Challenge can help you Switch to PBCs as a Data Scientist💪 While most candidates focus on basic SQL queries, top product-based companies often test advanced SQL concepts like window functions and query optimization. These are areas where many professionals struggle. It covers real-world scenarios like customer analytics, sales tracking, and data cleansing. Practicing these problems will help you think like a data scientist. Register here: https://bit.ly/3DTlJXm *Download notes* *helpful for Data Field*

LeetCode SQL 50 can help experienced professionals land at top PBCs! While most focus on basic JOINs, top tech firms test advanced SQL—window functions, CTEs, recursion, and optimization—that many professionals overlook. 😓 I have witnessed many data scientists who have aced MAANG interviews. The difference? They didn’t just study SQL; they focused on the most important challenges. If you're preparing for Data Scientist roles and need mentorship, Register here and download PDF details notes: https://bit.ly/4ixV29j

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Big MNC is hiring For Freshers and experience with great CTC! First, come first, so don’t be late. Apply now, https://bit.ly/41bQJLa